AI-ready flexible buildings to minimise carbon intensity
Purpose of project
Buildings can act like batteries, absorbing, storing and releasing heat into their materials. By utilising this “thermal inertia”, it is possible to lower a building’s carbon emissions and increase renewable energy consumption. However, to truly benefit from this phenomenon, we must develop smarter buildings with better automation and responsiveness to climate, renewable resources, and electricity grid prices.
This project seeks to create an automated simulation model of office buildings that optimises energy consumption for heating and cooling. This solution will help reduce 10-30% of energy requirements for heating and cooling and greater alignment with renewable electricity generation periods, resulting in a significant annual emissions reduction – roughly 83,000 tCO2-e. When the model has been completed, it will be made freely available.
Artificial Intelligence (AI) holds the potential to change the way we analyse building response data. Currently, the proprietary systems controlling buildings and their bespoke data models make it difficult for AI applications to access this information. This results in costly manual efforts to create custom response models. This project seeks to change that by defining and testing methods for organising building control datasets that are accessible to cloud-based analytics and optimisation. By doing this, we can unlock the full potential of AI in building management, improving energy efficiency and reducing carbon intensity.
Impact of project
Through this innovative project, we anticipate accelerated integration of flexible load control systems in commercial buildings. In the short term, the project should lead to a daily shiftable load ranging from 10% to 30% per building, though this may be reduced during severe weather conditions. The available flexibility will largely depend on the building’s insulation quality, with well-insulated structures generating greater benefits. This load flexibility will result in a decrease in carbon intensity, subject to the accuracy of forecast predictions and models. One of the intended outcomes of this project is to evaluate the potential benefits of adopting a carbon intensity forecast over a price-based one.
Project partners – industry and research
Monash University (Lead), Buildings Alive